Tagged Questions

I am doing orthogonal regression. My X matrix consists of returns on a broad market index, value index, growth index, a few sectors,.....(my Y is the returns on an equity fund)
I am regressing the Y ...

I want to use PCA for rich/cheap analysis of interest rates. For this I did the PCA on the time series of daily difference in interest rates, which is stationary. I cant do pca on levels, as they are ...

I have financial assets with totally different volatilities, thus I must standardize them before PCA, otherwise, assets with high variance may be considered as principle components, which is wrong.
...

Interest rate time series seems to be non-stationary whenever test is performed
But covariance or correlation matrix is derived from term structure time series which are non stationary and later PCA ...

Can someone check my proof? I think there is something not quite right. I have found limited resources online for this as well so I think it might benefit others to get this on the internet.
Assume ...

I'm attempting to use PCA to hedge a small fixed income portfolio. I start with one particular bond and chose the nearest other bond to hedge the 1st principle component. This decreases the portfolio ...

I have 19 currency pairs like USD.AUD, USD.CAD, etc. Also 82 cross currency pairs like AUD.CAD, EUR.AUD,EUR.CAD etc.
When I look to their graphs, most look similar, so I want to reduce number of pair ...

I came across a paper, not sure it originated from academia or a blog or such, that reported on applying principal components to build currency baskets from a set of individual currency pairs and to ...

When doing the PCA analysis, you end up with eigenvalues which are ordered by how much variance they explained for each eigenvector. Say, the eigenvectors since they are orthogonal, do not represent ...

I stumbled over the term Non-negative matrix factorization in presentations such as Application of Machine Learning to Finance and this Big Data in Asset Management.
The basic idea is to decompose a ...

I have calculated weights of selected assets in a market-neutral portfolio (presumably with min variance) using PCA and simple data covariance matrix.
The question is :
It is obvious that Cov Matrix ...

PCA seems to be very popular in dimension reduction applications and for extracting the top PCs which explain the data. One such application in futures is on the term structure to obtain the level, ...

I was given the returns of a cross-asset class portfolio of ETFs and I conducted PCA to obtain factors on dates from T-n, T-3, T-2,..., T. What I would like to do is decompose the market moves from ...

Let's assume we have a portfolio containing large number (~500) of risk factors. We want to simulate the portfolio dynamics. PCA based simulation would be faster as we can reduce the dimensionality. ...

Can anyone give me a few pointers of how to approach using PCA for trading? In particular, it seems to me, PCA is useful for selecting a subset of a portfolio of stocks(or other) rather than trading ...

In Meucci's paper called "Managing Diversification" he mentions that:
"Indeed, the eigenvalues A correspond to the variances of these uncorrelated portfolios"
I tried to replicate it but found they ...

Why is it so popular to use market capitalization weighted indices instead of taking the first principle component that explains the most variation of the constituents? I haven't yet seen an academic ...

I have a time series of data that is 300 days long. I compute PCA factor loadings on a moving window of 30 days. There are 7 stocks in the universe. Thus factors F1 through F7 are calculated on each ...

I'm trying to build a simple risk model for stocks using PCA. I've noticed that when my dimensions are larger than the number of observations (for example 1000 stocks but only 250 days of returns), ...

I built risk models using cluster analysis in a previous life. Years ago I learned about principal component analysis and I've often wondered whether that would have been more appropriate. What are ...